code: def __init__(self, args: dict):
      BaseEncoder.__init__(self)
      self.input_dim = args.input_dim
      self.latent_dim = args.latent_dim
  
      layers = nn.ModuleList()
  
      layers.append(
          nn.Sequential(
              nn.Linear(np.prod(args.input_dim), 512), nn.ReLU()
          )
      )
  
      self.layers = layers
      self.depth = len(layers)
  
      self.embedding = nn.Linear(512, self.latent_dim)
      self.log_var = nn.Linear(512, self.latent_dim)
  
imports: from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.models.nn import BaseDecoder, BaseDiscriminator, BaseEncoder, BaseMetric
  from ..base.base_utils import ModelOutput
label_desc: Use image node_id 1 to create a new Encoder_VAE_MLP object from inherited base classes: BaseEncoder with example: obj = Encoder_VAE_MLP(args)